Cs

Sa

b

Cc

a

ARRA

KCSFOS

1

ccopanmac

fe[etwtet

iB

1d

Chemical Engineering Journal 184 (2012) 16– 22

Contents lists available at SciVerse ScienceDirect

Chemical Engineering Journal

jo u r n al hom epage: www.elsev ier .com/ locate /ce j

opper and nickel competitive biosorption simulation from single and binaryystems by Sargassum filipendula

.J. Kleinübinga,b,∗, E. Guibalb, E.A. da Silvac, M.G.C. da Silvaa

School of Chemical Engineering, University of Campinas, UNICAMP, Cidade Universitária Zeferino Vaz, CEP 13081-970 Campinas, SP, BrazilEcole des Mines d’Alès, Laboratoire Génie de l’Environnement Industriel, Equipe Interfaces Fonctionnalisées pour l’Environnement et la Sécurité, 6 avenue de Clavières, F-30319 Alèsedex, FranceSchool of Chemical Engineering, Western State University of Paraná, UNIOESTE, Rua da Faculdade 645, Jardim La Salle, CEP 85903-000 Toledo, PR, Brazil

r t i c l e i n f o

rticle history:eceived 17 August 2011eceived in revised form 8 November 2011ccepted 9 November 2011

a b s t r a c t

The study focuses on the simulation of dynamic biosorption in single and binary systems containingCu(II) and Ni(II) ions using Sargassum filipendula (a marine algae). The Langmuir equilibrium models forsingle and binary systems were used to represent the equilibrium between solid and liquid phases. Amathematical model was used to describe the biosorption in fixed-bed columns. This model considers

eywords:ompetitive biosorptionargassum filipendulaixed-bed columns

that the mass transfer in the biosorbent is the controlling step, which is described by the LDF (LinearDriving Force) concept. The mathematical model adequately fitted the breakthrough curves for bothmonocomponent and binary systems. The higher affinity of marine algae biomass for Cu(II) (over Ni(II))was confirmed by the presence of an overshoot on Ni(II) breakthrough curves. The output detected in theexperimental breakthrough curves was also predicted by the mathematical model.

vershoot

imulation

. Introduction

Electroplating is one of the industries that produce the highestoncentrations of heavy metals in its wastewater. Electroplatingonsists in depositing thin layers of a metal on surfaces of metalr non-metal bodies by chemical or electrochemical means. Theserocesses are commonly used in the production of materials suchs automotive parts, jewellery, furniture accessories, tubular fur-iture, clothing accessories and footwear, electrical components,etal fittings, fixtures and weaponry, among others. The wastew-

ter produced in these processes contains copper and nickel in aomplex solution.

A number of different processes exist for removing metal ionsrom aqueous solutions, including precipitation [1–4], liquid/liquidxtraction [5–7], chelating or ion exchange resins and adsorbents1,8–10], and impregnated resins [11,12]. These processes arefficient for the removal of metal ions from concentrated solu-ions. However, they face some limitations in order to complyith regulation (residual concentration, sorbent life cycle, valoriza-

ion of precipitation sludges) and/or economy (competitiveness,tc.) issues. Biosorption may represent an alternative to conven-ional processes. This process is characterized by active and passive

∗ Corresponding author at: School of Chemical Engineering, University of Camp-nas, UNICAMP, Cidade Universitária Zeferino Vaz, CEP 13081-970 Campinas, SP,razil.

E-mail address: jaianak@gmail.com (S.J. Kleinübing).

385-8947/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.cej.2011.11.023

© 2011 Elsevier B.V. All rights reserved.

removal of heavy metals from diluted aqueous systems in biologi-cal materials by several mechanisms that differ quantitatively andqualitatively, depending on the species used, the origin of biomassand its processing. It can also be characterized as a process of rapidand reversible reaction of metal ions with the polymer functionalgroups of the cell wall [13]. In Sargassum seaweed, carboxyl andsulphate groups (present in alginic and fucoidan acids) be activein the ion exchange. The carboxylic groups presented in biomassconstitute the highest percentage of sites in dry seaweed (typicallyaround 70%) and play an important role in bioadsorption, beingresponsible for approximately 90% immobilization of metal in theprocess [14]. The bioadsorption capacity of the algae is directlyproportional to the presence of these sites in the alginate poly-mer.

Biosorption can be operated not only in batch reactors but also infixed-bed reactors. The column system offers optimum conditionsfor enhancing the driving force (due to concentration gradient),which, in turn, improves biosorption efficiency and mass transferperformance [15].

The design, operation and optimization of biosorption processin fixed-bed column can be more easily accomplished by the useof mathematical models that can predict the behavior of break-through curves under different operating conditions (pH, feedconcentration, composition, volumetric flow rate, etc.) [16].

Different models for the simulation of the dynamics of metalion biosorption by different types of biosorbents are available inthe literature [15,17–22]. Some researchers have used analyticalsolution models, especially the Bohart and Adams [23] and Thomas

S.J. Kleinübing et al. / Chemical Engine

Nomenclature

bj constant related to adsorption energy (L mmol−1)Cj concentration of ion j in the fluid phase (mmol L−1)Cout

jconcentration of ion j in the outlet of the column

(mmol L−1)C0

jinitial concentration of ion j in the column

(mmol L−1)C∗

jconcentration of metal ion j in solution at equilib-

rium (mmol L−1)Cfeed

jfeed concentration of species j in the fluid phase

(mmol L−1)KSj

Overall effective mass transport coefficient (min−1)L length of the fixed bed (cm)mS dry mass of bioadsorbent material (g)qj concentration of ion j in the biosorbent (mmol g−1)qj concentration of species j in the bioadsorbent parti-

cle (mmol g−1)q∗

jconcentration of ion j in biosorbent at equilibrium

(mmol g−1)qm maximum adsorbed amount of ion per unit of adsor-

bent mass to form a complete monolayer on thesurface (q = qm), (mmol g−1)

Q̇ volumetric flow rate of metal solution (cm3 min−1)t time (min)u interstitial velocity (cm min−1)z spatial coordinate in the direction of the bed height

(cm)εL void fraction of the bed�bed bed density, solid mass on a dry basis per bed vol-

ume (g L−1)� Dimensionless length (z L−1)� Dimensionless time (tu L−1)StD Stanton number (KSj

Lu−1)

[b

bmBtetatdsd

astcrfii

tit

2.5. Mathematical modeling

24] Models. However, these models have the main limitations ofeing only applicable to mono-component sorption.

The rigorous mathematical modeling of multi-componentiosorption in fixed-bed column requires removing the effects ofass transfer and isotherm equilibrium in breakthrough curves.

esides, equations are necessary to obtain the parameters of massransfer. In most adsorption processes, the characterization ofquilibrium is much more important than the effects of massransfer rates [25]. The mechanisms involved in fixed bed columndsorption are the following: intra-particle diffusion, external massransfer around the biosorbent particle (film diffusion) and hydro-ynamic axial dispersion. Some studies on metal removal in columnystems have shown that the effect of mass transfer due to axialispersion can be neglected [17,26].

It is recognized that real effluents rarely present solutions with single-metal species, but a variety of ions that compete for theame binding sites as the bioadsorbent material in the bioadsorp-ion process do. Depending on the composition of the effluent, theompetition effects may significantly reduce the efficiency of theemoval process. In multi-component systems, the challenge is tond models that can simulate the competitive adsorption of these

ons.This study reports the modeling of the breakthrough curves for

he sorption of Cu(II) and Ni(II) by seaweed Sargassum filipendula

n fixed-bed adsorption columns in mono- and bi-component sys-ems.

ering Journal 184 (2012) 16– 22 17

2. Materials and methods

2.1. S. filipendula

Seaweed S. filipendula was collected on the northern coast of SãoPaulo (São Sebastião) by the Center for Marine Biology, from theUniversity of São Paulo (CEBIMAR). After collection, algal biomasswas washed with distilled water, and dried at 60 ◦C for 24 h beforebeing stored. Biomass was separated by crushing and sieving par-ticles with an average diameter of 0.855 mm.

2.2. Metal solution

For this study, synthetic solutions of Cu(II) and Ni(II) wereprepared from nickel nitrate (Ni(NO3)2·6H2O) and copper nitrate(Cu(NO3)2·3H2O) salts.

2.3. Dynamic tests in column

For studies in fixed-bed reactor, an acrylic column was used(internal diameter: 1.1 cm; column height: 11 cm). The experi-ments were performed at 25 ◦C. The columns were filled with 3 gof seaweed (average diameter: 855 �m). The column was fed witha peristaltic pump at a flow rate of 5 mL min−1. Initial pH of thesolution was adjusted to 4.5 with nitric acid HNO3 (0.5 M). Themetal solution (Cu(II) and/or Ni(II)) was fed upwardly in the col-umn. To obtain biosorption equilibrium data, the experiments infixed-bed columns were carried out to complete biosorbent satu-ration that occurs when the outlet metal concentration reaches aconstant value (equal to the feed concentration). Column effluentsamples were collected in pre-defined intervals.

The amount of metal ion removed at equilibrium in the biosorp-tion process in fixed-bed column was calculated using Eq. (1),considering the breakthrough curves experimentally obtained.

q∗j =

CFeedj

Q̇

1000 ms

∫ tend

0

(1 −

Coutj

CFeedj

)dt (1)

The integration of Eq. (1) was numerically solved using the softwareORIGIN 7.0.

The concentration of metal ions was determined by InductivelyCoupled Plasma – Atomic Emission Spectroscopy (ICP-AES), usinga JY 2000 (Jobin-Yvon, Longjumeau, France) spectrometer.

2.4. Bed porosity

The column void fraction ε was determined by the measureof the void volume corresponding to the volume of demineral-ized water required to fill up the bed (interstitial volume betweenbiosorbent particles). At the end of each experiment, the solutionpresent inside the column was removed. The exhaustion of thesolution was reached by draining the column for a minimum periodof 24 h. Afterwards, a peristaltic pump (Cole Parmer) fed the columnfrom a reservoir that contained a defined volume of distilled water.The bed void fraction was calculated using the following equation:

εL = VV

VB(2)

where VV is the bed void volume and VB is the bed volume.

In order to develop the model, the following hypotheses wereassumed:

1 ngine

••••••

TpEc

C

C

Ipftf

w

q

TtL

q

q

AfLfi

fsecsDTb

mSbcwm

K

Tftmi

F

8 S.J. Kleinübing et al. / Chemical E

Isothermal processConstant porosity of the bedConstant physical propertiesNegligible resistance to radial mass transferPlug flow with no loss of charge, andNegligible axial dispersion

his mathematical model considered a differential mass balanceerformed in column for each species j in fluid phase represented byq. (3), under the initial condition given by Eq. (4) and the boundaryondition, according to Eq. (5).

∂Cj

∂�+ �bed

(1 − εL)εL

∂qj

∂�= ∂Cj

∂�(3)

j(�, 0) = C0j (4)

j(0, �) = CFeedj where � = 0 (5)

n order to describe the biosorption rate of species j in the solidhase, two models can be used: the resistance to intra-particle dif-usion or the resistance to diffusion across the liquid film aroundhe particle. The mass transfer rate is represented by a linear drivingorce (LDF) model according to:

∂qj

∂�= −StD(qj − q∗

j ) (6)

ith the following initial condition:

j(�, 0) = q0 (7)

he equilibrium relationship between the phases is represented byhe Langmuir isotherm model (Eq. (8)), for mono-components andangmuir with inhibition (Eq. (9)) for binary systems:

∗j =

qmbjC∗j

1 + bjC∗j

(8)

∗1 = qmC∗

1b1[1 + (K/b1)C∗2]

1 + b1C∗1 + b2C∗

1 + 2KC∗1C∗

2(9)

ccording to Kleinübing et al. [27], the equilibrium data obtainedor the single-system conditions were satisfactorily fitted by theangmuir model, whilst the binary system conditions were besttted by Langmuir model with inhibition (Eq. (9)).

The finite volume method was used for the resolution of dif-erential equation systems. The method consists in dividing theystem into volume elements and incorporating the differentialquations of the model in each volume in time and space. The dis-retization of this equation system into (ne) elements resulted in aystem of ordinary equations, which was solved using subroutineASSL [28], whose source code is in FORTRAN computer language.his code solves algebraic/differential equation systems and usesackward differentiation formulas.

Silva et al. [17] experimentally investigated and developed theathematical modeling of Cu(II) removal in fixed-bed column by

argassum sp. seaweed. The authors obtained a linear relationshipetween the mass transfer coefficient in solid phase and ion con-entration in the feeding solution. Based on these results, Eq. (10)as used in the mono-component breakthrough curves to calculateass transfer coefficient in the solid phase.

Sj= aj + bjCFj

(10)

he values for parameters aj and bj in Eq. (10) were estimatedrom the experimental data gathered from breakthrough curves inhe mono-component system. These parameters were obtained by

inimizing an objective function, represented by Eq. (10), employ-ng the Simplex Down Hill Method [29] optimization method.

OBJ = F1 + F2 + · · · + FN (11)

ering Journal 184 (2012) 16– 22

where N is the number of experimental breakthrough curves, Fiis the square error value for the ion output variable at the outletof the column related to experiment i, calculated by the followingequation.

Fi =np∑

k=1

(CEXPoutk

− CMODoutk

)2

(12)

where CEXPoutk

is the metal ion concentration at the outlet of the col-

umn experimentally measured, CMODoutk

is the metal ion concentrationat the outlet of the column calculated by the model and np is thenumber of experimental data.

The mass transfer coefficient in the solid phase (KSj) for the

binary system was calculated using the following equations:

KS1 = KSM1 + amix1

√CF1 CF2 (13)

KS2 = KSM2 + amix2

√CF1 CF2 (14)

where index 1 refers to Cu(II) and index 2 to Ni(II),(

KSM

)is the mass

transfer coefficient calculated for the mono-component system (Eq.(11)), amix1

and amix2are adjusted parameters estimated from the

experimental data from the binary rupture curves.These parameters were obtained by minimizing an objective

function, represented by Eq. (11), employing the Simplex DownHill Method [29] optimization method. In this case, Fi is the squareerror value for the experimental curve i, which for the binary exper-iments is given by the following equation:

Fi =np∑

k=1

[(CEXP

1k− CMOD

2k)2 + (CEXP

2k− CMOD

2k)2]

(15)

3. Results and discussion

3.1. Mathematical modeling for mono-component biosorption

The breakthrough curves for Cu(II) (Fig. 1a) and Ni(II)(Fig. 1b) were obtained for different feed concentrations.These experimental data were used for calculating theamounts of metal sorbed at biomass saturation. The Lang-muir isotherm (Eq. (9)) was used for describing singlesystems: the maximum adsorption capacity reached up to1.324 ± 0.0351 mmol g−1 for Cu(II) (whilst the Langmuir constantwas 4.673 ± 0.707 L mmol−1). For Ni(II), the maximum sorptioncapacity was close to 1.070 ± 0.0163 mmol L−1 (Langmuir constantof 4.065 ± 0.332 L mmol−1). The correlation coefficient (R2) reached0.99 for both metals [27].

Coefficient b, also called affinity coefficient, is analogous to asolute partition coefficient between liquid and solid phases. Theaffinity coefficient value increases with the interaction strength anddepends on the temperature. Langmuir equation parameters showthat S. filipendula has a slightly higher affinity for Cu(II) than forNi(II).

In the model used to describe the breakthrough curves in mono-component systems, the mass transfer coefficient of ion in the solidphase was estimated from the experimental curves. The correlationobtained with the feed ion concentration is presented in Eq. (16)for Cu(II) and in Eq. (17) for Ni(II).

KS1 = 2.45 × 10−3 + 3.28 × 10−3 CF1 (16)

KS2 = 2.85 × 10−3 + 2.05 × 10−3 CF2 (17)

where KS (min−1) and CF (mmol L−1).Fig. 1 shows the breakthrough curves for the sorption of Cu(II) (a)

and Ni(II) (b) ions, using S. filipendula. Solid lines show the curvessimulated with the mathematical model: the model fits well the

S.J. Kleinübing et al. / Chemical Engineering Journal 184 (2012) 16– 22 19

0 600 1200 1800 240 0 300 0 3600

0.0

0.2

0.4

0.6

0.8

1.0C

/CFe

ed

time (min)

4.18 mmol L-1

3.00 mmol L-1

2.12 mmol L-1

1.51 mmol L-1

0.97 mmol L-1

0.50 mmol L-1

0.29 mmol L-1

Mod el

0 500 1000 1500 200 0 2500 3000

0.0

0.2

0.4

0.6

0.8

1.0

C/C

Feed

time (min)

4.01 mm ol L-1

3.00 mm ol L-1

2.15 mm ol L-1

1.50 mm ol L-1

1.08 mm ol L-1

0.50 mm ol L-1

0.30 mm ol L-1

Model

akthr

e0maa

dctsa

cmts

3

beC

sfbq

(qif1

tochfo

tp

)a(

Fig. 1. Experimental and simulated bre

xperimental data. For Cu(II) ion at the lowest concentration (i.e.,.29 mmol L−1), the model fails to fit experimental data in the inter-ediary stage of the breakthrough curve. This probably means that

t a low metal concentration the resistance to mass transfer playsn active role in controlling sorption.

Obviously, an increase in the metal concentration results in aecrease in the breakthrough time. The slope of the breakthroughurve is influenced by the relationship between equilibrium sorp-ion isotherm and the mass transfer inside the column. Curves withteep slopes indicate high affinity of the metal with the biosorbent,s well as little resistance to mass transfer.

An increase in the concentration of the metal increases the con-entration gradient, which, in turn, enhances the mass transfer ofetal ions from the solution to the center of sorbent particles. Both

he breakthrough and the saturation occur faster, leading to steeplopes when metal concentration increases.

.2. Mathematical modeling for bi-component biosorption

Fifteen binary sorption experiments were carried out in fixed-ed columns with different fractions of Cu(II) and Ni(II). Thexperimental data were used for calculating the amounts of bothu(II) and Ni(II) adsorbed at biomass saturation [27].

The Langmuir model with inhibition (Eq. (10)) was used forimulating the equilibrium distribution of both Cu(II) and Ni(II)or biosorption in binary solutions [27]. The parameter values are:Cu = 5.83L mmol−1, bNi = 0.32 mmol−1, k = 0.75 (L mmol−1)−2 andm = 1.22 mmol g−1 (objective function = 1.30).

The maximum biosorption capacity of metal ions (Cu(II) + Ni(II))i.e., cumulative sorption capacity) when mixed in solution wasm = 1.22 mmol g−1 (according to the Langmuir model). This is anntermediate value between the maximum biosorption capacityor mono-component solutions (i.e., 1.32 mmol g−1 for Cu (II) and.07 mmol g−1 for Ni(II)) [27].

In the Langmuir model with inhibition, constant k is related tohe ratio between the formation rate and the consumption ratef complex BMCuMNi. It appears that for this model, the value foronstant k (0.75) is below the value for constant bCu (5.83) andigher than the value for constant bNi (0.32). This indicates that the

ormation of complex BMCu is more favorable than the formation

f complexes BMNiMCu and BMNi.

In the model used to describe the breakthrough curves inhe binary systems, the mass transfer coefficient for ion in solidhase was estimated from experimental breakthrough curves. The

)b(

ough curves for Cu(II) (a) and Ni(II) (b).

correlation obtained due to the concentration of feed ions is pre-sented in Eq. (18) for Cu(II) and in Eq. (19) for Ni(II).

KS1 = 2.45 × 10−3 + 3.28 × 10−3CF1 + 6.50 × 10−3√

CF1 CF2 (18)

KS2 = 2.85 × 10−3 + 2.05 × 10−3CF2 + 2.51 × 10−2√

CF1 CF2 (19)

where KS (min−1) and CF (mmol L−1).Fig. 2(a)–(d) shows breakthrough curves for binary systems,

obtained experimentally and by simulation using the parametersof the model that best fitted the experimental data, for both Cu(II)and Ni(II). Fig. 3(a and b) shows two conditions under which themodel satisfactorily fitted only the breakthrough curves for Cu(II).This conclusion was expected, since the Langmuir model showedgreater variations (compared to experimental values) for Ni(II)(compared to Cu(II)).

The breakthrough curves for bi-component solutions show dif-ferent shapes than those observed for mono-component tests(Fig. 1a and b). This can be attributed to the direct influence ofmetal cations that compete for a limited number of binding siteson biomass. Initially, both Ni(II) and Cu(II) are absorbed by the algae.However, with the course of the process, Cu(II) progressively dis-places Ni(II) ions that were previously adsorbed. The release ofNi(II) ions results in outlet concentrations of the metal that arehigher than the concentrations of the feed solution. This type ofbreakthrough curves (characterized by an overshoot) reflects thephenomenon of sequential exchange in which a more selectivecation can remove another previously exchanged cation from thesite. This phenomenon is typically found in competitive sorptionprocesses.

Similar effects were observed by Guibal et al. [30] and Chas-sary et al. [31] for Pd(II) and Pt(IV) recovery from bi-componentmixtures using different chitosan derivatives. The Pt(IV) displace-ment profiles by Pd(II) was attributed to the greater affinity of thesorbents for Pd(II) against Pt(IV).

Kast and Otten [32] directly correlate the overshoot to thedifference in the propagation velocities of moving fronts for thecompetitor species. The less favorable species propagates with thefaster moving front at first; the situation changes when the sec-ond component is adsorbed and thereby displaces part of the firstcomponent (resulting in a higher outlet concentration). When thesecond component breaks through, displacement of the first com-

ponent is reduced, and the concentrations of both components tendtowards their inlet values [30,31].

For a better understanding of the binary model, Fig. 4 schemati-cally shows the development of the binary mixture Cu(II) and Ni(II)

20 S.J. Kleinübing et al. / Chemical Engineering Journal 184 (2012) 16– 22

0 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

C/C

Feed

time (min)

Cu(II)(1.41 mmol L-1) Ni(II)(1.70 mmol L-1) Model

0 10 0 20 0 30 0 40 0 50 0 60 0 70 0 80 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

C/C

Feed

time (min)

Cu(II)(2.21 mmol L-1) Ni(II)(0.77 mmol L-1) Model

)b( )a(

0 20 0 40 0 60 0 80 0 100 0 120 0 140 0 160 0 180 0 200 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

C/C

Feed

time (min)

Cu(II)( 0.483 mm ol L-1) Ni(II)( 1.443 mm ol L-1) Mode l

0 20 0 40 0 60 0 80 0 100 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

C/C

Feed

time (min)

Cu(II)( 1.476 mm ol L-1

Ni(II)( 0.479 mm ol L-1) Mode l

)d( )c(

F ted cuC

bpcsbfNd

ig. 2. Breakthrough curves for binary solutions (experimental (points) and simulaT = 1.926 and (d) CT = 1.955, in mmol L−1.

ioadsorption. In Fig. 4, step (a) represents the initial stage of therocess (the raw biosorbent is present in the column). In (b), theolumn begins to be fed with the binary solution (Cu(II) and Ni(II));ince the biosorbent has a higher affinity for Cu(II), this metal will

e preferentially retained in the lower region (b-I) of the bed. There-ore, the rising solution becomes (relatively) more concentrated ini(II), which is practically adsorbed under mono-component con-ition (that is, no competition in the region (c-II)).

0 30 0 60 0 90 0 120 0 150 0 180 0 210 0 240 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

C/C

Feed

time (min)

Cu(II)(0.716 mmol L-1) Ni(II)(0.7324 mmol L-1) Model

)a(

Fig. 3. Breakthrough curves – experimental and simulated under binary conditions, b

rves (solid lines)), by the model, under the conditions: (a) CT = 3.11, (b) CT = 2.98, (c)

As the region (I) is saturated, the binary solution trickles throughregion II and most of Ni(II) that is initially sorbed is displacedby Cu(II) and released to the solution due to the greater copperaffinity for the biosorbent. This behavior characterizes the compet-

itive bioadsorption. Nickel passes through the section with a higherconcentration and is once more biosorbed in the posterior region.The sequence (c–h) of the competitive (I) and mono-component(II) bioadsorption processes continues until it reaches the

0 30 0 60 0 90 0 120 0 150 0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

C/C

Feed

time (min)

Cu(II)(1.10 mmol L-1) Ni(II)(0.375 mmol L-1) Model

)b(

y the model, under the conditions: (a) CT = 1.45 and (b) CT = 1.475, in mmol L−1.

S.J. Kleinübing et al. / Chemical Engineering Journal 184 (2012) 16– 22 21

ess of

stt

tbccpt

4

mSlcibicemfists

A

fifi

[

[

[

[[

[

Fig. 4. Scheme of the adsorption proc

aturation of the column with metal ions with a greater concen-ration of Cu(II) in adsorbent due to their respective affinities forhe sites of biosorbent.

Since the equilibrium model (Langmuir with inhibition) appliedo the dynamic modeling uses only binary data, it cannot predict theehavior of mono-component biosorption of Ni(II) occurring in theolumn. Consequently, greater deviations in the simulation of Ni(II)urves were observed. For Cu(II), in which biosorption always hap-ens with competition, the model was more appropriately fitted tohe experimental data.

. Conclusion

This study investigates the sorption of Cu(II) and Ni(II) fromono- and bi-component solutions in fixed-bed columns using

. filipendula biomass. Breakthrough curves are accurately simu-ated by a mathematical model based on the Linear Driving Forceoncept (LDF). This model appropriately represented the dynam-cs of individual and binary removal. The higher affinity of Cu(II) forioadsorbent was confirmed by the values obtained from the binary

sotherm parameters, and by the presence of an overshoot for theompetitor metal (i.e., Ni(II)) on the breakthrough curves. Both thexperimental data and the curves simulated by the mathematicalodel show this overshoot. The seaweed S. filipendula can be used

or the efficient removal of heavy metals in fixed-bed columns fromndustrial wastewater containing, individually, Cu(II) and Ni(II) inolution. When these ions are mixed, a partially selective biosorp-ion of Cu(II) occurs, though the selectivity is not sufficient foreparating the two metals.

cknowledgements

The authors would like to acknowledge CNPq and FAPESP for thenancial support and CEBIMar (USP) for the collection and identi-cation of the algal material.

[

[

Cu(II) + Ni(II) in the fixed bed column.

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